The global spread of (mis)information on spiders
Bibliographic record
Abstract
Abstract In the Internet era, the digital architecture that keeps us connected and informed may collaterally amplify the spread of misinformation and falsehood1,2. The magnitude of this problem is gaining global relevance3, as evidence accumulates that misinformation interferes with democratic processes and undermines collective responses to environmental and health crises4,5. Therefore, understanding how misinformation generates and spreads is becoming a pressing scientific, societal, and political challenge3. Advances in this area are delayed because high-resolution data on coherent information systems are difficult and time-consuming to acquire at global scales. We collated a high-resolution database of online newspaper articles on spider-human interactions. Spiders are widely feared animals6 that frequently appear in the spotlight of the global press7,8. Our database is unique in that it covers a global scale (5,348 news articles from 81 countries and 40 languages) while providing an expert-based assessment of the content and quality of each news article9. Here, we first show that the quality of news on spiders is exceedingly poor—47% of articles contained different types of error and 43% were sensationalistic—and we consolidate a quantitative understanding of the relationship between article quality and different news-level features. Among other factors, the consultancy of spider experts, but not doctors and other professionals, decrease sensationalism. Next, we show that the flow of spider-related information occurs within a highly interconnected global network and provide evidence that sensationalism, along with other predictors including numbers of spider species and internet users in a country, are key factors underlying the spread of information. Our results improve understanding of the drivers of (mis)information across broad-scale networks. They also represent a starting point to formulate recommendations for improving journalism quality. In the specific case of spiders, a more accurate media framing would translate into measurable benefits, limiting resource waste and mitigating human-wildlife conflicts and the prevalence of widespread arachnophobic sentiments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".